thursday/evolopy/FFA.py

# -*- coding: utf-8 -*-
"""
Created on Sun May 29 00:49:35 2016

@author: hossam
"""

#% ======================================================== %
#% Files of the Matlab programs included in the book:       %
#% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms,  %
#% Second Edition, Luniver Press, (2010).   www.luniver.com %
#% ======================================================== %
#
#% -------------------------------------------------------- %
#% Firefly Algorithm for constrained optimization using     %
#% for the design of a spring (benchmark)                   %
#% by Xin-She Yang (Cambridge University) Copyright @2009   %
#% -------------------------------------------------------- %

import numpy
import math
import time
from .solution import solution


def alpha_new(alpha, NGen):
    #% alpha_n=alpha_0(1-delta)^NGen=10^(-4);
    #% alpha_0=0.9
    delta = 1 - (10 ** (-4) / 0.9) ** (1 / NGen)
    alpha = (1 - delta) * alpha
    return alpha


def FFA(objf, lb, ub, dim, n, MaxGeneration):

    # General parameters

    # n=50 #number of fireflies
    # dim=30 #dim
    # lb=-50
    # ub=50
    # MaxGeneration=500

    # FFA parameters
    alpha = 0.5  # Randomness 0--1 (highly random)
    betamin = 0.20  # minimum value of beta
    gamma = 1  # Absorption coefficient
    if not isinstance(lb, list):
        lb = [lb] * dim
    if not isinstance(ub, list):
        ub = [ub] * dim

    zn = numpy.ones(n)
    zn.fill(float("inf"))

    # ns(i,:)=Lb+(Ub-Lb).*rand(1,d);
    ns = numpy.zeros((n, dim))
    for i in range(dim):
        ns[:, i] = numpy.random.uniform(0, 1, n) * (ub[i] - lb[i]) + lb[i]
    Lightn = numpy.ones(n)
    Lightn.fill(float("inf"))

    # [ns,Lightn]=init_ffa(n,d,Lb,Ub,u0)

    convergence = []
    s = solution()

    print('FFA is optimizing  "' + objf.__name__ + '"')

    timerStart = time.time()
    s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")

    # Main loop
    for k in range(0, MaxGeneration):  # start iterations

        #% This line of reducing alpha is optional
        alpha = alpha_new(alpha, MaxGeneration)

        #% Evaluate new solutions (for all n fireflies)
        for i in range(0, n):
            zn[i] = objf(ns[i, :])
            Lightn[i] = zn[i]

        # Ranking fireflies by their light intensity/objectives

        Lightn = numpy.sort(zn)
        Index = numpy.argsort(zn)
        ns = ns[Index, :]

        # Find the current best
        nso = ns
        Lighto = Lightn
        nbest = ns[0, :]
        Lightbest = Lightn[0]

        #% For output only
        fbest = Lightbest

        #% Move all fireflies to the better locations
        #    [ns]=ffa_move(n,d,ns,Lightn,nso,Lighto,nbest,...
        #          Lightbest,alpha,betamin,gamma,Lb,Ub);
        scale = []
        for b in range(dim):
            scale.append(abs(ub[b] - lb[b]))
        scale = numpy.array(scale)
        for i in range(0, n):
            # The attractiveness parameter beta=exp(-gamma*r)
            for j in range(0, n):
                r = numpy.sqrt(numpy.sum((ns[i, :] - ns[j, :]) ** 2))
                # r=1
                # Update moves
                if Lightn[i] > Lighto[j]:  # Brighter and more attractive
                    beta0 = 1
                    beta = (beta0 - betamin) * math.exp(-gamma * r ** 2) + betamin
                    tmpf = alpha * (numpy.random.rand(dim) - 0.5) * scale
                    ns[i, :] = ns[i, :] * (1 - beta) + nso[j, :] * beta + tmpf

        # ns=numpy.clip(ns, lb, ub)

        convergence.append(fbest)

        IterationNumber = k
        BestQuality = fbest

        if k % 1 == 0:
            print(
                ["At iteration " + str(k) + " the best fitness is " + str(BestQuality)]
            )
    #
    ####################### End main loop
    timerEnd = time.time()
    s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
    s.executionTime = timerEnd - timerStart
    s.convergence = convergence
    s.optimizer = "FFA"
    s.bestIndividual = nbest
    s.objfname = objf.__name__

    return s